Machine vision detection and identification of vehicles is used within many traffic applications, including tolling, traffic data collection, and vehicle search or tracking. Machine vision systems, such as those using visible light or infrared (IR) cameras, can be used to identify vehicles by capturing and reading an identifier from a license plate or number plate (used interchangeably herein) on the vehicle. Machine vision systems can also be used to generally detect the presence of a vehicle and to identify the type of vehicle or a class into which the vehicle fits. Identification of the class of a vehicle is useful for many reasons, such as confirming that the unique identifier on the license plate is on the correct type of vehicle (i.e., the license plate is not stolen), and for knowing how much a vehicle should be charged in a tolling or traffic congestion scheme based on the type of vehicle. One method for detecting and classifying vehicles is to use edge detection, as described in U.S. Pat. No. 5,434,927 to Brady et al., incorporated herein by reference. Brady et al. describes the use of detecting edges of vehicles in images to classify vehicles. However, such classification is typically based on comparing edges from a captured image to an existing database of edge data related to various classes. With new vehicles being designed and manufactured every year, it becomes cumbersome, time consuming, and susceptible to errors to update an existing database. United States Patent Publication Number 2013/0246321 to Pandit et al. discusses a classification refinement tool; however, the mechanism described refines manual classification of assets, but does not automatically create new classes.